Sold by: MphasisÂ
Deployed on AWS
Restaurant Reviews Topic Extraction is a deep learning algorithm which can extract up to 14 types of aspects from restaurant reviews.
Overview
This solution identifies the various aspects a reviewer is mentioning when providing a review for any restaurant business. This can help businesses easily identify which are its most prominent aspects (e.g. price, ambience, taste, quality etc.) which are getting reviewed and what are the associated opinions about them. They can then improve on these aspects to provide a superior customer experience.
Highlights
- This solution is trained on a large publicly available dataset of restaurant reviews. Algorithm follows unsupervised attention model based deep learning approach to learn the important keywords in relation with the associated sentence. These words are then arranged into 14 different disjoint sets of aspects and then ranked as per usability in the dataset. New aspects are inferred w.r.t to comparative association of the new words with model learnt set of words per aspect type.
- Due to unavailability or self-biased behaviour of tagged data, unsupervised approaches of deep learning models are gaining popularity. Restaurant Reviews Topic Extraction is an unsupervised attention based deep learning model, which can pay more attention to impactful words in the dataset. Our algorithm is re-trainable w.r.t to client’s data. Currently, algorithm can identify 14 different types of aspects. Number of aspect types is also tuneable while training the model as per client’s requirement. These capabilities of client specific tuning and inference makes model personalized.
- Mphasis DeepInsights is a cloud-based cognitive computing platform that offers data extraction & predictive analytics capabilities. Need Customized Deep learning and Machine Learning Solutions? Get in Touch!
Details
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Delivery method
Latest version
Deployed on AWS
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Pricing
Pricing is based on actual usage, with charges varying according to how much you consume. Subscriptions have no end date and may be canceled any time.
Additional AWS infrastructure costs may apply. Use the AWS Pricing Calculator to estimate your infrastructure costs.
Dimension | Description | Cost/host/hour |
|---|---|---|
ml.m5.large Inference (Batch) Recommended | Model inference on the ml.m5.large instance type, batch mode | $20.00 |
ml.m5.large Inference (Real-Time) Recommended | Model inference on the ml.m5.large instance type, real-time mode | $10.00 |
ml.m4.4xlarge Inference (Batch) | Model inference on the ml.m4.4xlarge instance type, batch mode | $20.00 |
ml.m5.4xlarge Inference (Batch) | Model inference on the ml.m5.4xlarge instance type, batch mode | $20.00 |
ml.m4.16xlarge Inference (Batch) | Model inference on the ml.m4.16xlarge instance type, batch mode | $20.00 |
ml.m5.2xlarge Inference (Batch) | Model inference on the ml.m5.2xlarge instance type, batch mode | $20.00 |
ml.p3.16xlarge Inference (Batch) | Model inference on the ml.p3.16xlarge instance type, batch mode | $20.00 |
ml.m4.2xlarge Inference (Batch) | Model inference on the ml.m4.2xlarge instance type, batch mode | $20.00 |
ml.c5.2xlarge Inference (Batch) | Model inference on the ml.c5.2xlarge instance type, batch mode | $20.00 |
ml.p3.2xlarge Inference (Batch) | Model inference on the ml.p3.2xlarge instance type, batch mode | $20.00 |
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Delivery details
Amazon SageMaker model
An Amazon SageMaker model package is a pre-trained machine learning model ready to use without additional training. Use the model package to create a model on Amazon SageMaker for real-time inference or batch processing. Amazon SageMaker is a fully managed platform for building, training, and deploying machine learning models at scale.
Deploy the model on Amazon SageMaker AI using the following options:
Version release notes
Updated with new features
Additional details
Inputs
- Summary
The model takes a .txt file as input which contains all the reviews.
- Input MIME type
- text/csv, application/json, text/plain
https://github.com/Mphasis-ML-Marketplace/Restaurant-Reviews-Topic-Extraction
https://github.com/Mphasis-ML-Marketplace/Restaurant-Reviews-Topic-Extraction
Input data descriptions
The following table describes supported input data fields for real-time inference and batch transform.
Field name | Description | Constraints | Required |
|---|---|---|---|
reviews | Contains list of reviews | Type: FreeText | Yes |
Resources
Vendor resources
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